WO2019140373A1 - Système et procédé de modélisation probabiliste - Google Patents

Système et procédé de modélisation probabiliste Download PDF

Info

Publication number
WO2019140373A1
WO2019140373A1 PCT/US2019/013471 US2019013471W WO2019140373A1 WO 2019140373 A1 WO2019140373 A1 WO 2019140373A1 US 2019013471 W US2019013471 W US 2019013471W WO 2019140373 A1 WO2019140373 A1 WO 2019140373A1
Authority
WO
WIPO (PCT)
Prior art keywords
requestor
probabilistic
content
modeling process
probabilistic model
Prior art date
Application number
PCT/US2019/013471
Other languages
English (en)
Inventor
Benjamin W. Vigoda
Glynnis Kearney
Pawel Jerzy ZIMOCH
Matthew C. BARR
Martin Blood Zwirner FORSYTHE
Original Assignee
Gamalon, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Gamalon, Inc. filed Critical Gamalon, Inc.
Publication of WO2019140373A1 publication Critical patent/WO2019140373A1/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/219—Managing data history or versioning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/26—Visual data mining; Browsing structured data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/334—Query execution
    • G06F16/3346—Query execution using probabilistic model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/901—Indexing; Data structures therefor; Storage structures
    • G06F16/9024—Graphs; Linked lists
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
    • G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/10—Text processing
    • G06F40/197—Version control
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/205—Parsing
    • G06F40/216—Parsing using statistical methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/237—Lexical tools
    • G06F40/247—Thesauruses; Synonyms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/40—Processing or translation of natural language
    • G06F40/55—Rule-based translation
    • G06F40/56—Natural language generation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0283—Price estimation or determination
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M3/00—Automatic or semi-automatic exchanges
    • H04M3/42—Systems providing special services or facilities to subscribers
    • H04M3/487—Arrangements for providing information services, e.g. recorded voice services or time announcements
    • H04M3/493—Interactive information services, e.g. directory enquiries ; Arrangements therefor, e.g. interactive voice response [IVR] systems or voice portals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G06N5/022—Knowledge engineering; Knowledge acquisition
    • G06N5/025—Extracting rules from data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q2220/00—Business processing using cryptography
    • G06Q2220/10—Usage protection of distributed data files
    • G06Q2220/12—Usage or charge determination

Definitions

  • Bad service branch 124 may lead to bad service node 130, which may be configured to process the portion of content 56 (e.g.. unstructured text-based customer feedback) that concerns (in whole or in part) bad feedback concerning the customer service of restaurant 58.
  • bad service node 130 may define bad service word list 132 that may include e.g., the word bad, as well as synonyms of (and words related to) the word bad (e.g., rude, mean, jerk, exotic, and scowling).
  • location branch 108 may lead to location node 116, which may be configured to process the portion of content 56 (e.g.. unstructured text-based customer feedback) that concerns (in whole or in part) feedback concerning the location of restaurant 58.
  • location node 116 may define location word list 148 that may include e.g., words indicative of the location of restaurant 58.
  • a portion of content 56 e.g., a text-based customer feedback message
  • that portion of content 56 may be considered to be text-based customer feedback concerning the location of restaurant 58 and (therefore) may be routed to location node 116 of probabilistic model 100 for further processing.
  • probabilistic model 100 includes two branches off of location node 116, namely: good location branch 150 and bad location branch 152.
  • Bad location branch 152 may lead to bad location node 158, which may be configured to process the portion of content 56 (e.g.. unstructured text-based customer feedback) that concerns (in whole or in part) bad feedback concerning the location of restaurant 58.
  • bad location node 158 may define bad location word list 160 that may include words indicative of restaurant 58 being in a bad location. Accordingly and in the event that a portion of content 56 (e.g., a text-based customer feedback message) that was routed to location node 116 includes any of the words defined within bad location word list 160, that portion of content 56 may be considered to be text-based customer feedback indicative of restaurant 58 being in a bad location (and, therefore, may be routed to bad location node 158).
  • a portion of content 56 e.g., a text-based customer feedback message
  • Examples of such preprocessing may include but are not limited to: the correction of spelling errors (e.g., to correct any spelling errors within text-based feedback and to correct any transcription errors within voice-based feedback), the inclusion of additional synonyms, and the removal of irrelevant comments.
  • user content e.g., feedback 60
  • probabilistic modeling process 10 may identify any pertinent content that is included within feedback 60.
  • probabilistic modeling process 10 may identify the pertinent content (included within feedback 60) as the phrase“my dinner was appealing” and may ignore / remove the irrelevant content“my cab got stuck in traffic”.
  • feedback 60 includes the word“dinner”
  • probabilistic modeling process 10 may rout feedback 60 to meal node 114 via meal branch 106.
  • feedback 60 also includes the word “yummy”
  • probabilistic modeling process 10 may rout feedback 60 to good meal node 140 via good meal branch 136 and may consider feedback 60 to be text-based customer feedback indicative of a good meal being received at restaurant 58.
  • Probabilistic modeling process 10 may be configured to automatically define probabilistic model 100 based upon content 56. Accordingly, probabilistic modeling process 10 may receive content (e.g., a very large quantity of text-based messages). Probabilistic modeling process 10 may be configured to define one or more probabilistic model variables for probabilistic model 100. For example, probabilistic modeling process 10 may be configured to allow a user of probabilistic modeling process 10 to specify such probabilistic model variables. Another example of such variables may include but is not limited to values and/or ranges of values for a data flow variable. For the following discussion and for this disclosure, examples of “variable” may include but are not limited to variables, parameters, ranges, branches and nodes.
  • probabilistic modeling process 10 may be configured to allow a user to define a) a weighting on branches off of a branching node; b) a weighting on values of a variable in the model; c) the maximum number of branching node branches as e.g., five, d) the minimum number of branching node branches as e.g., three and/or e) the quantity of branching node branches as e.g., four.
  • probabilistic modeling process 10 may process content 56 to identify the pertinent content included within content 56. As discussed above, probabilistic modeling process 10 may identify the pertinent content (included within content 56) and may ignore / remove the irrelevant content.
  • probabilistic modeling process 10 may define various lists (e.g., lists 128, 132, 142, 146, 156, 160, 170, 174) by starting with a root word (e.g., good or bad) and may then determine synonyms for this words and use those words and synonyms to populate lists 128, 132, 142, 146, 156, 160, 170, 174.
  • lists 128, 132, 142, 146, 156, 160, 170, 174 may be defined by starting with a root word (e.g., good or bad) and may then determine synonyms for this words and use those words and synonyms to populate lists 128, 132, 142, 146, 156, 160, 170, 174.
  • probabilistic modeling process 10 may define a first version of the probabilistic model (e.g., probabilistic model 100) based, at least in part, upon pertinent content found within content 56. Probabilistic modeling process 10 may compare the first version of the probabilistic model (e.g., probabilistic model 100) to content 56 to determine if the first version of the probabilistic model (e.g., probabilistic model 100) is a good explanation of the content.
  • probabilistic modeling process 10 may use an ML algorithm to fit the first version of the probabilistic model (e.g., probabilistic model 100) to the content, wherein examples of such an ML algorithm may include but are not limited to one or more of: an inferencing algorithm, a learning algorithm, an optimization algorithm, and a statistical algorithm.
  • probabilistic modeling process 10 may define a revised version of the probabilistic model (e.g., revised probabilistic model 100’).
  • probabilistic modeling process 10 may e.g., adjust weighting, adjust probabilities, adjust node counts, adjust node types, and/or adjust branch counts to define the revised version of the probabilistic model (e.g., revised probabilistic model 100’).
  • probabilistic modeling process 10 may be configured to allow for ML objects to be utilized when generating probabilistic model 100 an/or probabilistic model 100’.
  • probabilistic model 100 includes four branches off of branching node 102, namely: service branch 104; meal branch 106; location branch 108; and value branch 110 that respectively lead to service node 112, meal node 114, location node 116, and value node 118.
  • service branch 104 leads to service node 112 (which is configured to process service-based content); meal branch 106 leads to meal node 114 (which is configured to process meal-based content); location branch 108 leads to location node 116 (which is configured to process location-based content); and value branch 110 leads to value node 118 (which is configured to process value-based content).
  • probabilistic modeling process 10 when probabilistic modeling process 10 is defining probabilistic model 100 (based upon content 56), probabilistic modeling process 10 may utilize one or more ML objects (chosen from plurality of ML objects 64 defined within ML object collection 62).
  • probabilistic modeling process 10 may identify 202 the need for an ML object within probabilistic model 100 that may process the service-based content (i.e., effectuate the functionality of portion 176). Accordingly, probabilistic modeling process 10 may access 204 ML object collection 62 that defines plurality of ML objects 64 and search for ML objects that may process service-based content. Assume that upon accessing 204 ML object collection 62, probabilistic modeling process 10 may identify ML object 66 as an ML object that may (potentially) process service-based content.
  • probabilistic modeling process 10 may obtain 206 a first ML object (e.g., ML object 66) selected from plurality of ML objects 64 defined within ML object collection 62. Probabilistic modeling process 10 may then test 208 the first ML object (e.g., ML object 66) with probabilistic model 100.
  • a first ML object e.g., ML object 66
  • Probabilistic modeling process 10 may then test 208 the first ML object (e.g., ML object 66) with probabilistic model 100.
  • probabilistic modeling process 10 may add 214 the first ML object (e.g., ML object 66) to probabilistic model 100 using a transfer learning methodology.
  • transfer learning is a technique that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cats could apply when trying to recognize dogs.
  • FIG. 4C A graphical example of such a transfer learning methodology (being used to analyze a picture of an animal to determine if the animal is a dog or a cat) is shown in FIG. 4C.
  • two separate probabilistic models may be arranged in parallel.
  • the first model is trained using labelled pictures of e.g., cats.
  • the trained first model is then reused as the starting point for the second model and is trained using labelled pictures of e.g., dogs. So the second model utilizes knowledge from the first model...but the two models are not combined.
  • Probabilistic modeling process 10 may determine 218 whether the first ML object (e.g., ML object 66) is applicable with probabilistic model 100. Continuing with the above-stated example in which probabilistic modeling process 10 adds 208 the first ML object (e.g., ML object 66) to probabilistic model 100, probabilistic modeling process 10 may determine 218 whether the first ML object (e.g., ML object 66) is applicable with probabilistic model 100 by performing the comparisons discussed above.
  • probabilistic modeling process 10 may generate a large quantity of messages e.g., by auto-generating messages using the above-described probabilities, nodes, node types, and words, resulting in generated content 56’. Generated content 56’ may then be compared to content 56 to determine if probabilistic model 100 (with ML object 66 being utilized to effectuate portion 176) is a good explanation of the content. For example, if generated content 56’ exceeds a threshold level of similarity to content 56, probabilistic model 100 (with ML object 66 being utilized to effectuate portion 176) may be deemed a good explanation of the content. Conversely, if generated content 56’ does not exceed a threshold level of similarity to content 56, probabilistic model 100 (with ML object 66 being utilized to effectuate portion 176) may be deemed not a good explanation of the content.
  • probabilistic modeling process 10 may maintain (e.g., permanently incorporate) the first ML object (e.g., ML object 66) within probabilistic model 100 and may (if needed) continue defining probabilistic model 100 (in e.g., the manner described above).
  • probabilistic modeling process 10 may add 230 the additional ML object (e.g., ML object 68) to probabilistic model 100 using a transfer learning methodology.
  • transfer learning is a technique that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cats could apply when trying to recognize dogs
  • probabilistic modeling process 10 may add 232 the additional ML object (e.g., ML object 68) to probabilistic model 100 using a Bayesian synthesis methodology.
  • Bayesian synthesis is a technique in which individual models are combined. This way, the combined models each know the confidence level of the other model. So a model that has a high confidence level may still defer to the other model if that other model has a higher confidence level.
  • probabilistic modeling process 10 may be configured to automate the searching of ML object collection 62 so that an ML object applicable with a probabilistic model (e.g., probabilistic model 100) may be identified.
  • a probabilistic model e.g., probabilistic model 100
  • probabilistic modeling process 10 may identify 202 a need for an ML object within a probabilistic model (e.g., probabilistic model 100). Specifically and as discussed above, assume that after probabilistic modeling process 10 defines the four branches off of branching node 102 (e.g., service branch 104, meal branch 106, location branch 108, and value branch 110), probabilistic modeling process 10 identifies 202 the need for an ML object within probabilistic model 100 that may process service-based content (i.e., effectuate the functionality of portion 176 of probabilistic model 100 that is configured to process the service-based content within content 56).
  • a probabilistic model e.g., probabilistic model 100.
  • probabilistic modeling process 10 may request 252 permission to utilize the first ML object (e.g., ML object 66).
  • probabilistic modeling process 10 may notify a user (e.g., administrator 70 of probabilistic modeling process 10) that an ML object (e.g., ML object 66) was identified 250 that may (potentially) process the service-based content within content 56, asking for permission to utilize the same.
  • Probabilistic modeling process 10 may determine 218 whether the first ML object (e.g., ML object 66) is applicable with probabilistic model 100 by performing the above-described comparisons. As discussed above, probabilistic modeling process 10 may use an ML algorithm to fit probabilistic model 100 to the content, wherein examples of such an ML algorithm may include but are not limited to one or more of: an inferencing algorithm, a learning algorithm, an optimization algorithm, and a statistical algorithm.
  • probabilistic modeling process 10 may test 258 the additional ML object (e.g., ML object 68) with the probabilistic model (e.g., probabilistic model 100) and may determine 260 (in the manner described above) whether the additional ML object (e.g., ML object 68) is applicable with the probabilistic model (e.g., probabilistic model 100).
  • probabilistic modeling process 10 identifies 202 the need for an ML object within probabilistic model 100 that may process service-based content (i.e., effectuate the functionality of portion 176 of probabilistic model 100 that is configured to process the service-based content within content 56).
  • Probabilistic modeling process 10 may obtain 306 the specific ML object (e.g., ML object 66) if a requestor (e.g., a user) meets / accepts the access criteria of the specific ML object (e.g., ML object 66).
  • this access criteria may define a usage fee for the specific ML object (e.g., ML object 66) and meeting / accepting the access criteria may include the requestor (e.g., a user) agreeing to pay the usage fee.
  • the requestor e.g., a user
  • Probabilistic modeling process 10 may add 308 the specific ML object (e.g., ML object 66) to the probabilistic model (e.g., probabilistic model 100). Once added 308, probabilistic modeling process 10 may determine (in the manner described above) whether the specific ML object (e.g., ML object 66) is applicable with the probabilistic model (e.g., probabilistic model 100).
  • probabilistic modeling process 10 may be configured to maintain and link a plurality of versions of an ML object in a fashion similar to a version management system for documents or software.
  • probabilistic modeling process 10 may maintain 350 an ML object collection (e.g., ML object collection 62), wherein ML object collection 62 may define at least one ML object (e.g., plurality of ML objects 64).
  • ML object collection 62 may define at least one ML object (e.g., plurality of ML objects 64).
  • each ML object included within plurality of ML objects 64 and defined within ML object collection 62 may be a portion of a probabilistic model that may be configured to effectuate a specific functionality (in a fashion similar to that of a software object used in object oriented programming).
  • ML object collection 62 may be any structure that defines / includes a plurality of ML objects, examples of which may include but are not limited to an ML object repository or another probabilistic model.
  • At least one of the ML objects (e.g., ML object 66) defined within plurality of ML objects 64 may define a plurality of linked versions of the ML object (e.g., a plurality of temporally varying versions of the ML object).
  • ML object 66 includes three versions, namely: the current version (e.g., ML object 66), an older version (e.g., ML object 66.1), and an oldest version (e.g., ML object 66.2).
  • Such version criteria may define the type of user who can access a specific linked version of an ML object (e.g., ML object 66). Accordingly and within a company, certain versions of ML objects may be available to people belonging to certain groups or teams, while the same versions of ML objects may be unavailable to people on other groups or teams. Further and within a company, certain versions of ML objects may be available to people that have a certain level, permission, key or authority, wherein e.g. management level users may be able to access certain versions of ML objects, while the same versions of ML objects may be unavailable to non management level users. Additionally, certain versions of ML objects may be available to various users provided that the users are not associated with a competitor of the owner of the versions of ML object.
  • Probabilistic modeling process 10 may further be configured to: restrict 354 access to one or more of the linked versions (e.g., ML object 66, 66.1, 66.2) of the ML object based, at least in part, upon the version criteria; and/or grant 356 access to one or more of the linked versions (e.g., ML object 66, 66.1, 66.2) of the ML object based, at least in part, upon the version criteria.
  • the linked versions e.g., ML object 66, 66.1, 66.2
  • the requester e.g., the user
  • the requestor status may include one or more of: the requestor being associated with a group; the requestor being associated with an entity; the requestor being associated with a class; the requestor being associated with a level; the requestor having one or more required keys; the requestor having one or more required permissions; and the requestor having a certain authority.
  • probabilistic modeling process 10 may be configured to allow the usage of one or more of plurality of ML objects 64 defined within ML object collection 62 to be controlled / regulated.
  • probabilistic modeling process 10 may maintain 200 an ML object collection (e.g., ML object collection 62), wherein ML object collection 62 may define plurality of ML objects 64.
  • ML object collection 62 may define plurality of ML objects 64.
  • each ML object included within plurality of ML objects 64 and defined within ML object collection 62 may be a portion of a probabilistic model that may be configured to effectuate a specific functionality (in a fashion similar to that of a software object used in object oriented programming).
  • ML object collection 62 may be any structure that defines / includes a plurality of ML objects, examples of which may include but are not limited to an ML object repository or another probabilistic model.
  • Probabilistic modeling process 10 may associate 400 usage criteria with each of plurality of ML objects 64.
  • plurality of ML objects 64 may include one or more discrete and unique ML objects (e.g., ML object 66, 68) and/or one or more unique versions of a common ML object (e.g., ML objects 66, 66.1, 66.2).
  • probabilistic modeling process 10 may be configured to associate 400 usage criteria with each of plurality of ML objects 64 to regulate the usage of an ML object within plurality of ML objects 64.
  • usage criteria may define the type of user who can access a particular ML object.
  • certain ML objects may be available to people belonging to certain groups or teams, while the same ML objects may be unavailable to people on other groups or teams. Further and within a company, certain ML objects may be available to people that have a certain level, permission, key or authority, wherein e.g. management level users may be able to access certain ML objects, while the same ML objects may be unavailable to non-management level users. Additionally, certain ML objects may be available to various users provided that the user is not associated with a competitor of the owner of the ML object. Further still, certain ML objects may be available to various users provided that the various users are willing to pay a licensing / use fee. Accordingly and by associating such usage criteria with each of the ML objects included within plurality of ML objects 64, the usage to these individual ML objects may be controlled / regulated.
  • probabilistic modeling process 10 may access 204 the ML object collection (e.g., ML object collection 62) and may identify 402 a specific ML object chosen from the plurality of ML objects defined within the ML object collection (e.g., ML object collection 62). Assume that upon accessing 204 ML object collection 62, probabilistic modeling process 10 may identify 402 ML object 66 as an ML object that may (potentially) process service-based content within content 56. Additionally, probabilistic modeling process 10 may determine 404 the usage criteria associated with the specific ML object (e.g., ML object 66).
  • the specific ML object e.g., ML object 66
  • Probabilistic modeling process 10 may obtain 406 the specific ML object if a requestor meets / accepts the usage criteria of the specific ML object.
  • this usage criteria may define a usage fee for the specific ML object (e.g., ML object 66) and meeting / accepting the usage criteria may include the requestor (e.g., a user) agreeing to pay the usage fee.
  • the requestor e.g., a user
  • the requestor may need to agree to pay a $20 usage fee in order to use the specific ML object (e.g., ML object 66) within probabilistic model 100.
  • the usage criteria may define a requestor status and meeting / accepting the usage criteria may include the requestor (e.g., the user) meeting the requestor status.
  • the requestor e.g., the user
  • the requester may need to be on a certain team, be a member of a certain group, have a certain status, be employed by a certain company, etc.
  • the requestor status may include one or more of: the requestor being associated with a group; the requestor being associated with an entity; the requestor being associated with a class; the requestor being associated with a level; the requestor having one or more required keys; the requestor having one or more required permissions; and the requestor having a certain authority.
  • certain ML objects may only be usable by someone willing to pay a licensing fee, as the usage criteria may define the specific ML object (e.g., ML object 66) as requiring that a licensing be paid.
  • certain ML objects e.g., ML object 66
  • certain ML objects may only be usable by someone that is not in competition with the owner of the specific ML object (e.g., ML object 66), as the usage criteria may define no competitive overlap with respect to specific ML object (e.g., ML object 66).
  • ML object 66 is a customer satisfaction ML object that was developed by (and is owned by) ABC Coffee Roasters
  • the usage criteria associated with ML object 66 may prohibit XYZ Coffee Roasters from using ML object 66 (as they are competitors) while allowing Super Slice Pizza to use ML object 66 (as they are not competitors).
  • Probabilistic modeling process 10 may add 408 the specific ML object (e.g., ML object 66) to the probabilistic model (e.g., probabilistic model 100). Once added 408, probabilistic modeling process 10 may determine (in the manner described above) whether the specific ML object (e.g., ML object 66) is applicable with the probabilistic model (e.g., probabilistic model 100.
  • probabilistic modeling process 10 may be configured to allow the usage of one or more of plurality of ML objects 64 defined within ML object collection 62 to be partially automated by seeking user approval concerning the same.
  • probabilistic modeling process 10 may maintain 200 the ML object collection (e.g., ML object collection 62), wherein ML object collection 62 may define plurality of ML objects 64.
  • each ML object included within plurality of ML objects 64 and defined within ML object collection 62 may be a portion of a probabilistic model that may be configured to effectuate a specific functionality (in a fashion similar to that of a software object used in object oriented programming).
  • ML object collection 62 may be any structure that defines / includes a plurality of ML objects, examples of which may include but are not limited to an ML object repository or another probabilistic model.
  • Probabilistic modeling process 10 may allow 450 a plurality of entities to access an ML object collection (e.g., ML object collection 62) that defines plurality of ML objects 64.
  • ML object collection e.g., ML object collection 62
  • probabilistic modeling process 10 may be configured to allow 450 user 36, user 38, user 40 and/or user 42 to access ML object collection 62 that defines plurality of ML objects 64.
  • Probabilistic modeling process 10 may be configured to monitor the various ML objects (e.g., plurality of ML object 64) defined within ML object collection 62 to determine which (if any) of plurality of ML object 64 may be usable by one or more of the entities (e.g., users 36, 38, 40, 42) accessing the ML object collection (e.g., ML object collection 62).
  • ML objects e.g., plurality of ML object 64
  • probabilistic modeling process 10 may make 452 an inquiry to a first entity, chosen from the plurality of entities (e.g., users 36, 38, 40, 42), about a specific ML object defined within the ML object collection (e.g., ML object collection 62).
  • a first entity chosen from the plurality of entities (e.g., users 36, 38, 40, 42)
  • a specific ML object defined within the ML object collection e.g., ML object collection 62.
  • the first entity is user 36 who owns the specific ML object (e.g., ML object 66), wherein the inquiry made 452 by probabilistic modeling process 10 may concern whether the first entity (e.g., user 36) is interested in allowing a second entity (e.g., user 38), chosen from the plurality of entities (e.g., users 36, 38, 40, 42), to use the specific ML object (e.g., ML object 66).
  • a second entity e.g., user 38
  • the plurality of entities e.g., users 36, 38, 40, 42
  • ML object 66 is a customer satisfaction ML object that was developed by (and is owned by) ABC Coffee Roasters (with which user 36 is associated). Accordingly, if user 38 is associated XYZ Coffee Roasters (a competitor of ABC Coffee Roasters), user 36 may not be interested in allowing user 38 to use the specific ML object (e.g., ML object 66) and may negatively respond to the inquiry made 452 by probabilistic modeling process 10. However, if user 38 is associated with Super Slice Pizza (not a competitor of ABC Coffee Roasters), user 36 may be interested in allowing user 38 to use the specific ML object (e.g., ML object 66) and may positively respond to the inquiry made 452 by probabilistic modeling process 10.
  • the inquiry made 452 by probabilistic modeling process 10 may concern whether the first entity (e.g., user 36) is interested in maintaining the specific ML object (e.g., ML object 66) private.
  • the inquiry made 452 by probabilistic modeling process 10 may concern whether the first entity (e.g., user 36) is interested in maintaining the specific ML object (e.g., ML object 66) private (for competitive advantage purposes).
  • a second entity e.g., user 38
  • the plurality of entities e.g., users 36, 38, 40, 42
  • owns the specific ML object e.g., ML object 66
  • the inquiry made 452 by probabilistic modeling process 10 may concern whether the first entity (e.g., user 36) is interested in using the specific ML object (e.g., ML object 66).
  • probabilistic modeling process 10 may make 452 an inquiry to the first entity (e.g., user 36) concerning whether the first entity (e.g., user 36) is interested in using the specific ML object (e.g., ML object 66) owned by (in this example) the second entity (e.g., user 38).
  • probabilistic modeling process 10 may e.g., render a message on a display screen of client electronic devices 28 associated with user 36 to inquire as to whether e.g., user 36 is interested in using ML object 66.
  • probabilistic modeling process 10 may be configured to automate the generation of a list of synonym words that may be edited / revised by a user.
  • probabilistic modeling process 10 may define various lists (e.g., lists 128, 132, 142, 146, 156, 160, 170, 174) by starting with one or more root word and may then determine synonyms for these root word(s) and use those root words and synonyms to populate lists 128, 132, 142, 146, 156, 160, 170, 174.
  • probabilistic modeling process 10 may obtain 502 the word-based synonym ML object (e.g., ML object 68) from an ML object collection (e.g., ML object collection 62) that defines plurality of ML objects 64. Probabilistic modeling process 10 may then add 504 the word-based synonym ML object (e.g., ML object 68) to the probabilistic model (e.g., probabilistic model 100) and generate 506 a list of synonym words via the word-based synonym ML object (e.g., ML object 68).
  • ML object collection e.g., ML object collection 62
  • Probabilistic modeling process 10 may then add 504 the word-based synonym ML object (e.g., ML object 68) to the probabilistic model (e.g., probabilistic model 100) and generate 506 a list of synonym words via the word-based synonym ML object (e.g., ML object 68).
  • probabilistic modeling process 10 may provide 508 the word-based synonym ML object (e.g., ML object 68) with one or more starter words from which the list of synonym words is generated. For example, if the list of synonym words being generated 506 is seeded with the starter word “car”, probabilistic modeling process 10 may generate 506 a list of synonym words via the word-based synonym ML object (e.g., ML object 68) that may include e.g., automobile, limousine, convertible, wagon, hatchback, sedan, coupe, gas guzzler and hardtop.
  • the word-based synonym ML object e.g., ML object 68
  • probabilistic modeling process 10 may generate 510 the list of synonym words (for car) via a synonym word list (via e.g., a remotely accessible electronic thesaurus).
  • probabilistic modeling process 10 may generate 512 the list of synonym words (for car) via a synonym word algorithm (via e.g., a machine learning algorithm that processes content in order to identify words having similar meanings).
  • a synonym word algorithm via e.g., a machine learning algorithm that processes content in order to identify words having similar meanings.
  • Probabilistic modeling process 10 may enable 514 the list of synonym words to be edited by a user.
  • a user e.g., user 36, 38, 40, 42
  • the user may wish to edit the list of synonym words (for car) to e.g., remove“gas guzzler” and to add subcompact.
  • probabilistic modelling process 10 may receive 518 edits provided by the user (e.g., user 36, 38, 40, 42) via the client electronic device utilized by the user (e.g., user 36, 38, 40, 42), wherein these edits may be used to revise 520 the list of synonym words (for car).
  • probabilistic modeling process 10 may be configured to automate the generation of a list of synonym phrases that may be edited / revised by a user.
  • probabilistic modeling process 10 may identify 550 a need for a phrase-based synonym ML object (e.g., ML object 68) within a probabilistic model (e.g., probabilistic model 100).
  • a phrase -based synonym ML object e.g., ML object 68
  • ML object 68 may be utilized within probabilistic model 100 to generate multi-word (i.e., phrase) synonyms for one or more root words / phrases.
  • probabilistic modeling process 10 may provide 558 the phrase-based synonym ML object (e.g., ML object 68) with one or more starter phrases from which the list of synonym phrases is generated. For example, if the list of synonym phrases being generated 556 is seeded with the starter phrase“not happy”, probabilistic modeling process 10 may generate 556 a list of synonym phrases via the phrase-based synonym ML object (e.g., ML object 68) that may include e.g., totally impossible, abject failure, very disappointed, and f’ed up.
  • the phrase-based synonym ML object e.g., ML object 68
  • probabilistic modeling process 10 may generate 560 the list of synonym phrases (for not happy) via a synonym phrase list (via e.g., a remotely accessible electronic thesaurus).
  • Probabilistic modeling process 10 may enable 564 the list of synonym phrases to be edited by a user.
  • a user e.g., user 36, 38, 40, 42
  • the user may wish to edit the list of synonym phrases (for not happy) to e.g., remove f’ed up and to add somewhat underwhelmed.
  • probabilistic modelling process 10 may receive 568 edits provided by the user (e.g., user 36, 38, 40, 42) via the client electronic device utilized by the user (e.g., user 36, 38, 40, 42), wherein these edits may be used to revise 570 the list of synonym phrases (for not happy).
  • probabilistic modeling process 10 may read a portion of the messages included within content 56 and may determine that the portion of messages reviewed all seem to concern either a) the service, b) the meal, c) the location and/or d) the value of restaurant 58. Accordingly, probabilistic modeling process 10 may be configured to allow the user to define one or more probabilistic model variables, which (in this example) may include one or more probabilistic model branch variables.
  • Examples of such probabilistic model branch variables may include but are not limited to one or more of: a) a weighting on branches off of a branching node; b) a weighting on values of a variable in the model; c) a minimum acceptable quantity of branches off of the branching node (e.g., branching node 102); d) a maximum acceptable quantity of branches off of the branching node (e.g., branching node 102); and e) a defined quantity of branches off of the branching node (e.g., branching node 102).
  • probabilistic modeling process 10 may require and may utilize user-defined variables to define the initial structure of probabilistic model 100. However, and as will be discussed below in greater detail, probabilistic modeling process 10 may be configured to“jump start” the generation of probabilistic model 100 by importing an existing navigatable structure.
  • probabilistic modeling process 10 may identify 600 the need for a probabilistic model (e.g., probabilistic model 100) to process existing content (e.g., content 56).
  • a probabilistic model e.g., probabilistic model 100
  • probabilistic modeling process 10 may import 602 a navigatable structure (e.g., navigatable structure 72) and may utilize 604 the navigatable structure (e.g., navigatable structure 72) as a basis for an initial probabilistic model (i.e., the initial version or starting point of probabilistic model 100).
  • an interactive voice response (IVR) tree may define the manner in which telephone calls are routed within a call center.
  • a file directory structure may define the manner in which content is organized.
  • An analysis flowchart may define the manner in which issues are analyzed.
  • a data organization structure may define the manner in which an entity is organized.
  • probabilistic modeling process 10 may use 608 an ML algorithm to fit the initial probabilistic model (e.g., probabilistic model 100) to the existing content (e.g., content 56), wherein examples of such an ML algorithm may include but are not limited to one or more of: an inferencing algorithm, a learning algorithm, an optimization algorithm, and a statistical algorithm.
  • probabilistic modeling process 10 may generate new content (e.g., new content 56’) via the initial probabilistic model (i.e., probabilistic model 100 that is currently based on navigatable structure 72). Probabilistic modeling process 10 may then compare the new content (e.g., new content 56’) to the existing content (e.g., content 56) to determine whether the initial probabilistic model (i.e., probabilistic model 100 that is currently based on navigatable structure 72) is a good explanation of content 56.
  • new content e.g., new content 56
  • the existing content e.g., content 56
  • probabilistic modeling process 10 may utilize 612 the initial probabilistic model (i.e., probabilistic model 100 that is currently based on navigatable structure 72).
  • probabilistic modeling process 10 may modify 614 the initial probabilistic model (i.e., probabilistic model 100 that is currently based on navigatable structure 72) to make a revised probabilistic model (e.g., revised probabilistic model 100’). Probabilistic modeling process 10 may then determine 616 whether the revised probabilistic model (e.g., revised probabilistic model 100’) is a good explanation of the existing content (e.g., content 56).
  • probabilistic modeling process 10 may be configured to allow the usage of one or more of plurality of ML objects 64 defined within ML object collection 62 to be partially automated by seeking user advice concerning the same.
  • probabilistic modeling process 10 may access 204 an ML object collection (e.g., ML object collection 62) that defines plurality of ML objects 64 and may identify a specific ML object (e.g., ML object 66) chosen from plurality of ML objects 64 defined within the ML object collection (e.g., ML object collection 62). Assume that upon accessing 204 ML object collection 62, probabilistic modeling process 10 may identify ML object 66 as an ML object that may (potentially) process the service-based content within content 56.
  • probabilistic modeling process 10 may assign 650 a confidence level to a specific ML object (e.g., ML object 66), chosen from plurality of ML objects 64, concerning the applicability of the specific ML object (e.g., ML object 66) with the probabilistic model (e.g., probabilistic model 100).
  • a specific ML object e.g., ML object 66
  • the probabilistic model e.g., probabilistic model 100
  • probabilistic modeling process 10 may determine whether a specific ML object (e.g., ML object 66) is applicable with probabilistic model 100 by performing the above-described comparisons. Further and as discussed above, probabilistic modeling process 10 may use an ML algorithm to fit probabilistic model 100 to the content, wherein examples of such an ML algorithm may include but are not limited to one or more of: an inferencing algorithm, a learning algorithm, an optimization algorithm, and a statistical algorithm
  • probabilistic modeling process 10 may generate a very large quantity of messages e.g., by auto-generating messages using probabilistic model 100 (with ML object 66 installed), thus resulting in generated content 56’. Probabilistic modeling process 10 may then compare generated content 56’ to content 56 to determine if probabilistic model 100 (with ML object 66 installed) is a good explanation of content 56.
  • probabilistic modeling process 10 may determine that the specific ML object (e.g., ML object 66) is applicable with the probabilistic model (e.g., probabilistic model 100). This comparison (between generated content 56’ and content 56) may be considered, in whole or in part, when assigning 650 a confidence level to a specific ML object (e.g., ML object 66).
  • a low level of similarity between generated content 56’ and content 56 may result in probabilistic modeling process 10 assigning a low confidence level to the specific ML object (e.g., ML object 66).
  • an intermediate level of similarity between generated content 56’ and content 56 may result in probabilistic modeling process 10 assigning an intermediate confidence level to the specific ML object (e.g., ML object 66).
  • probabilistic modeling process 10 may determine 652 that the specific ML object (e.g., ML object 66) is not applicable with the probabilistic model (e.g., probabilistic model 100 with ML object 66 installed) when the confidence level assigned is in a low confidence level range.
  • the specific ML object e.g., ML object 66
  • the probabilistic model e.g., probabilistic model 100 with ML object 66 installed
  • probabilistic modeling process 10 may add 656 the specific ML object (e.g., ML object 66) to the probabilistic model (e.g., probabilistic model 100).
  • probabilistic modeling process 10 may determine 658 that the specific ML object (e.g., ML object 66) is possibly applicable with the probabilistic model (e.g., probabilistic model 100 with ML object 66 installed) when the confidence level assigned is in an intermediate confidence level range.
  • the specific ML object e.g., ML object 66
  • the probabilistic model e.g., probabilistic model 100 with ML object 66 installed
  • probabilistic modeling process 10 may request 660 guidance as to whether the specific ML object (e.g., ML object 66) should be utilized in the probabilistic model (e.g., probabilistic model 100).
  • probabilistic modeling process 10 may ask 662 a user (e.g., user 36) whether the specific ML object (e.g., ML object 66) should be utilized in the probabilistic model (e.g., probabilistic model 100). For example, probabilistic modeling process 10 may e.g., render a message on a display screen of client electronic devices 28 associated with user 36 to inquire as to whether e.g., user 36 is interested in using ML object 66.
  • probabilistic modeling process 10 may be configured to interact with a user to clarify specific uncertainties with respect to a probabilistic model (e.g., probabilistic model 100).
  • probabilistic model 100 may be used to e.g., process pictures of animals to determine if the animal in the picture is a dog or a cat.
  • the content e.g., content 56
  • probabilistic model 100 is processing content 56 to determine which (if any) of the pictures include within content 56 are pictures of dogs or pictures of cats.
  • the pictures within content 56 are categorized as pictures of dogs, pictures of cats, or pictures of other animals (i.e., not dogs or cats).
  • probabilistic modeling process 10 may identify 700 a specific uncertainty in a probabilistic model (e.g., probabilistic model 100).
  • this“specific uncertainty” is whether picture 74 is a picture of a cat.
  • probabilistic model 100 may deem this to be a“specific uncertainty” if e.g., the confidence level assigned / defined to picture 74 being a picture of a cat is below 95%.
  • probabilistic modeling process 10 may provide 702 a user (e.g., user 36) with one or more initial questions concerning the specific uncertainty (e.g., whether picture 74 is a picture of a cat).
  • probabilistic modeling process 10 may e.g., render a message on a display screen of client electronic devices 28 associated with user 36 to provide the one or more initial questions concerning the specific uncertainty (e.g., whether picture 74 is a picture of a cat).
  • the inquiry may be made verbally.
  • probabilistic modeling process 10 may provide 702 user 36 with the question:“Is this a picture of a cat?”.
  • Probabilistic modeling process 10 may receive 704 a response (e.g., response 76) from the user (e.g., user 36) concerning the one or more initial questions (e.g.,“Is this a picture of a cat?”) with respect to the specific uncertainty.
  • the response provided by the user e.g., user 36
  • the response (e.g., response 76) received from the user (e.g., user 36) with respect to the specific uncertainty may reduce the uncertainty level of the specific uncertainty.
  • this “specific uncertainty” is whether picture 74 is a picture of a cat. Accordingly and when the response (e.g., response 76) received from the user (e.g., user 36) with respect to the specific uncertainty reduces the uncertainty level of the specific uncertainty, response 76 may be“I am not sure but it looks like a cat”. Accordingly and in such a situation, the specific uncertainty (e.g., whether picture 74 is a picture of a cat) is not resolved by one portion of response 76 (namely,“I am not sure...”). However, the uncertainty level of the specific uncertainty is reduced by another portion of response 76 (namely“...but it looks like a cat”).
  • the response (e.g., response 76) received from the user (e.g., user 36) with respect to the specific uncertainty may include rule-based information that may be used with a future uncertainty.
  • this“specific uncertainty” is whether picture 74 is a picture of a cat. Accordingly and when the response (e.g., response 76) received from the user (e.g., user 36) with respect to the specific uncertainty includes rule-based information that may be used with a future uncertainty, response 76 may be“it is a cat because it has a short snout”.
  • the specific uncertainty e.g., whether picture 74 is a picture of a cat
  • one portion of response 76 namely,“it is a cat .
  • another portion of response 76 namely“...because it has a short snout”
  • probabilistic modeling process 10 assigns / defines a confidence level with respect to that certain picture (i.e., wherein the increase in confidence level associated with the animal having a short snout may be enough to raise the confidence level into the range that does not require user intervention).
  • probabilistic modeling process 10 may take 706 an action based, at least in part, upon the response (e.g., response 76) received 704 from the user (e.g., user 36). Examples of such an action taken 706 may include but are not limited to: clarifying the specific uncertainty (e.g., whether picture 74 is a picture of a cat) and providing the user (.e., user 36) with one or more additional questions concerning the specific uncertainty (e.g., whether picture 74 is a picture of a cat).
  • the present disclosure may be embodied as a method, a system, or a computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, the present disclosure may take the form of a computer program product on a computer-usable storage medium having computer-usable program code embodied in the medium.
  • the computer-usable or computer-readable medium may also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
  • a computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
  • the computer-usable medium may include a propagated data signal with the computer- usable program code embodied therewith, either in baseband or as part of a carrier wave.
  • the computer usable program code may be transmitted using any appropriate medium, including but not limited to the Internet, wireline, optical fiber cable, RF, etc.
  • Computer program code for carrying out operations of the present disclosure may be written in an object oriented programming language such as Java, Smalltalk, C++ or the like. However, the computer program code for carrying out operations of the present disclosure may also be written in conventional procedural programming languages, such as the "C" programming language or similar programming languages.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user’s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user’s computer through a local area network / a wide area network / the Internet (e.g., network 14).
  • These computer program instructions may also be stored in a computer-readable memory that may direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
  • the computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).
  • the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Business, Economics & Management (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Software Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Databases & Information Systems (AREA)
  • Development Economics (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Strategic Management (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Evolutionary Computation (AREA)
  • Probability & Statistics with Applications (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Game Theory and Decision Science (AREA)
  • Economics (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Medical Informatics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Computer Security & Cryptography (AREA)
  • Computer Hardware Design (AREA)
  • Bioethics (AREA)
  • Algebra (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Signal Processing (AREA)
  • Information Transfer Between Computers (AREA)
  • Machine Translation (AREA)

Abstract

L'invention concerne un procédé mis en œuvre par ordinateur, un produit programme informatique et un système informatique permettant de maintenir une collection d'objets ML qui définit une pluralité d'objets ML ; et d'associer des critères d'utilisation à chaque objet de la pluralité d'objets ML.
PCT/US2019/013471 2018-01-12 2019-01-14 Système et procédé de modélisation probabiliste WO2019140373A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US201862616784P 2018-01-12 2018-01-12
US62/616,784 2018-01-12
US201862617790P 2018-01-16 2018-01-16
US62/617,790 2018-01-16

Publications (1)

Publication Number Publication Date
WO2019140373A1 true WO2019140373A1 (fr) 2019-07-18

Family

ID=67212929

Family Applications (10)

Application Number Title Priority Date Filing Date
PCT/US2019/013459 WO2019140362A1 (fr) 2018-01-12 2019-01-14 Système et procédé de modélisation probabiliste
PCT/US2019/013503 WO2019140392A1 (fr) 2018-01-12 2019-01-14 Système et procédé de modélisation probabiliste
PCT/US2019/013455 WO2019140359A1 (fr) 2018-01-12 2019-01-14 Système et procédé de modélisation probabiliste
PCT/US2019/013453 WO2019140358A1 (fr) 2018-01-12 2019-01-14 Système et procédé de modélisation probabiliste
PCT/US2019/013471 WO2019140373A1 (fr) 2018-01-12 2019-01-14 Système et procédé de modélisation probabiliste
PCT/US2019/013499 WO2019140390A1 (fr) 2018-01-12 2019-01-14 Système et procédé de modélisation probabiliste
PCT/US2019/013483 WO2019140382A2 (fr) 2018-01-12 2019-01-14 Système et procédé de modélisation probabiliste
PCT/US2019/013488 WO2019140384A2 (fr) 2018-01-12 2019-01-14 Système et procédé de modélisation probabiliste
PCT/US2019/013495 WO2019140388A1 (fr) 2018-01-12 2019-01-14 Système et procédé de modélisation probabiliste
PCT/US2019/013472 WO2019140374A1 (fr) 2018-01-12 2019-01-14 Système et procédé de modélisation probaliste

Family Applications Before (4)

Application Number Title Priority Date Filing Date
PCT/US2019/013459 WO2019140362A1 (fr) 2018-01-12 2019-01-14 Système et procédé de modélisation probabiliste
PCT/US2019/013503 WO2019140392A1 (fr) 2018-01-12 2019-01-14 Système et procédé de modélisation probabiliste
PCT/US2019/013455 WO2019140359A1 (fr) 2018-01-12 2019-01-14 Système et procédé de modélisation probabiliste
PCT/US2019/013453 WO2019140358A1 (fr) 2018-01-12 2019-01-14 Système et procédé de modélisation probabiliste

Family Applications After (5)

Application Number Title Priority Date Filing Date
PCT/US2019/013499 WO2019140390A1 (fr) 2018-01-12 2019-01-14 Système et procédé de modélisation probabiliste
PCT/US2019/013483 WO2019140382A2 (fr) 2018-01-12 2019-01-14 Système et procédé de modélisation probabiliste
PCT/US2019/013488 WO2019140384A2 (fr) 2018-01-12 2019-01-14 Système et procédé de modélisation probabiliste
PCT/US2019/013495 WO2019140388A1 (fr) 2018-01-12 2019-01-14 Système et procédé de modélisation probabiliste
PCT/US2019/013472 WO2019140374A1 (fr) 2018-01-12 2019-01-14 Système et procédé de modélisation probaliste

Country Status (2)

Country Link
US (10) US20190220762A1 (fr)
WO (10) WO2019140362A1 (fr)

Families Citing this family (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190220762A1 (en) * 2018-01-12 2019-07-18 Gamalon, Inc. Probabilistic modeling system and method
TW202005312A (zh) * 2018-05-15 2020-01-16 美商萊特美特股份有限公司 用於訓練基於矩陣的可微分程式的系統及方法
US11709946B2 (en) 2018-06-06 2023-07-25 Reliaquest Holdings, Llc Threat mitigation system and method
US11095673B2 (en) 2018-06-06 2021-08-17 Reliaquest Holdings, Llc Threat mitigation system and method
US11537936B2 (en) * 2019-01-17 2022-12-27 Servicenow, Inc. Data set generation for testing of machine learning pipelines
US20210125068A1 (en) * 2019-10-28 2021-04-29 MakinaRocks Co., Ltd. Method for training neural network
US10979369B1 (en) * 2020-03-04 2021-04-13 Capital One Services, Llc Systems and methods for updating creatives generation models
US10915697B1 (en) * 2020-07-31 2021-02-09 Grammarly, Inc. Computer-implemented presentation of synonyms based on syntactic dependency

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060173985A1 (en) * 2005-02-01 2006-08-03 Moore James F Enhanced syndication
US20170063904A1 (en) * 2015-08-31 2017-03-02 Splunk Inc. Identity resolution in data intake stage of machine data processing platform

Family Cites Families (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7082426B2 (en) * 1993-06-18 2006-07-25 Cnet Networks, Inc. Content aggregation method and apparatus for an on-line product catalog
US7574409B2 (en) * 2004-11-04 2009-08-11 Vericept Corporation Method, apparatus, and system for clustering and classification
US7894677B2 (en) * 2006-02-09 2011-02-22 Microsoft Corporation Reducing human overhead in text categorization
WO2009061390A1 (fr) * 2007-11-05 2009-05-14 Enhanced Medical Decisions, Inc. Systèmes et procédés d'apprentissage automatique pour un traitement du langage naturel amélioré
US8266148B2 (en) * 2008-10-07 2012-09-11 Aumni Data, Inc. Method and system for business intelligence analytics on unstructured data
US20110112995A1 (en) * 2009-10-28 2011-05-12 Industrial Technology Research Institute Systems and methods for organizing collective social intelligence information using an organic object data model
US8972436B2 (en) * 2009-10-28 2015-03-03 Yahoo! Inc. Translation model and method for matching reviews to objects
US20130218884A1 (en) * 2012-02-21 2013-08-22 Salesforce.Com, Inc. Method and system for providing a review from a customer relationship management system
US20130282812A1 (en) * 2012-04-24 2013-10-24 Samuel Lessin Adaptive audiences for claims in a social networking system
US9501469B2 (en) * 2012-11-21 2016-11-22 University Of Massachusetts Analogy finder
US9454767B2 (en) * 2013-03-13 2016-09-27 Salesforce.Com, Inc. Systems, methods, and apparatuses for implementing a related command with a predictive query interface
US9958288B2 (en) * 2013-03-14 2018-05-01 Microsoft Technology Licensing, Llc Planning under destination uncertainty
US9646262B2 (en) * 2013-06-17 2017-05-09 Purepredictive, Inc. Data intelligence using machine learning
CN105528349B (zh) * 2014-09-29 2019-02-01 华为技术有限公司 知识库中问句解析的方法及设备
US20170364830A1 (en) * 2016-06-15 2017-12-21 Gamalon, Inc. Machine learning for automated organization system and method
US10984338B2 (en) * 2015-05-28 2021-04-20 Raytheon Technologies Corporation Dynamically updated predictive modeling to predict operational outcomes of interest
US10387765B2 (en) * 2016-06-23 2019-08-20 Siemens Healthcare Gmbh Image correction using a deep generative machine-learning model
US20190220762A1 (en) * 2018-01-12 2019-07-18 Gamalon, Inc. Probabilistic modeling system and method

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060173985A1 (en) * 2005-02-01 2006-08-03 Moore James F Enhanced syndication
US20170063904A1 (en) * 2015-08-31 2017-03-02 Splunk Inc. Identity resolution in data intake stage of machine data processing platform

Also Published As

Publication number Publication date
WO2019140359A1 (fr) 2019-07-18
WO2019140384A2 (fr) 2019-07-18
US20190220763A1 (en) 2019-07-18
WO2019140384A3 (fr) 2020-04-09
WO2019140374A1 (fr) 2019-07-18
WO2019140392A1 (fr) 2019-07-18
US20190220764A1 (en) 2019-07-18
WO2019140388A1 (fr) 2019-07-18
US20190220613A1 (en) 2019-07-18
WO2019140358A1 (fr) 2019-07-18
US20190220766A1 (en) 2019-07-18
WO2019140390A1 (fr) 2019-07-18
US20190220761A1 (en) 2019-07-18
US20190220767A1 (en) 2019-07-18
WO2019140382A3 (fr) 2020-04-09
US20190220518A1 (en) 2019-07-18
US20190220765A1 (en) 2019-07-18
US20190220517A1 (en) 2019-07-18
WO2019140382A2 (fr) 2019-07-18
WO2019140362A1 (fr) 2019-07-18
US20190220762A1 (en) 2019-07-18

Similar Documents

Publication Publication Date Title
US20190220762A1 (en) Probabilistic modeling system and method
CN110175227B (zh) 一种基于组队学习和层级推理的对话辅助系统
US20180052664A1 (en) Method and system for developing, training, and deploying effective intelligent virtual agent
US20180129978A1 (en) Machine learning data analysis system and method
CN107958317A (zh) 一种众包项目中选取众包参与人的方法和装置
CN111368789B (zh) 图像识别方法、装置、计算机设备和存储介质
US20210374276A1 (en) Smart document migration and entity detection
CN112579733B (zh) 规则匹配方法、规则匹配装置、存储介质及电子设备
CN113490930A (zh) 链接和处理不同的知识图
Windiatmoko et al. Developing FB chatbot based on deep learning using RASA framework for university enquiries
Zhang et al. Using large-scale heterogeneous graph representation learning for code review recommendations at microsoft
US20190197423A1 (en) Probabilistic modeling system and method
CN117171403A (zh) 数据处理方法、装置、计算机设备和存储介质
Kleebaum et al. Continuous rationale identification in issue tracking and version control systems
CN116701752A (zh) 基于人工智能的新闻推荐方法、装置、电子设备及介质
Elleuch et al. Discovering business processes and activities from messaging systems: State-of-the art
Piest et al. Smarter interoperability based on automatic schema matching and intelligence amplification
Lee et al. Customizing the capture of software architectural design decisions
Baclawski et al. Ontology Summit 2021 Communiqué: Ontology generation and harmonization
CN108876031B (zh) 一种软件开发者贡献值预测方法
US20230186162A1 (en) System for engagement of human agents for decision-making in a dynamically changing environment
Mendez et al. Software Quality: Higher Software Quality Through Zero Waste Development: 15th International Conference, SWQD 2023, Munich, Germany, May 23-25, 2023, Proceedings
Patel et al. Enhancing Career Development Utilizing LLM for Targeted Learning Pathway
Sabri Ayoub Leveraging Artificial Intelligence For Sustained Organizational Competitive Advantage: A Study In Natural Language Processing And Dynamic Capabilities
Badi et al. Model-Based Knowledge Management in HV Battery Development

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19738807

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 14.10.2020)

122 Ep: pct application non-entry in european phase

Ref document number: 19738807

Country of ref document: EP

Kind code of ref document: A1